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It is possible to use CAPM to determine the price of the security, Copeland and Weston (1992). Recall from 1 that that spot price of a security can be found by discounting the expected future value as Pt = E[PT]/(1 + ri)T-t. The dif culty in 1 was pinning down ri = rf + q, the risk-adjusted return. CAPM lets us put a number on the risk premium by using beta, so our pricing formula becomes Pt = E[PT ] . (2.2.9)



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Section V in w11x.. However, the cost would be prohibitively large. The PCAM chip w29x can search for the minimum timestamp value at very high speed. However, due to a sizable on-chip memory requirement, the PCAM is still too expensive to implement. Motivated by the need for efficient hardware implementation, w30x proposes a RAM-based search engine RSE., as explained below. The RSE reorganizes and stores all the V-bits in the calendar queue. As shown in Figure 5.12, its main function is to find a nonempty timing queue that has the smallest finish time address . and output its address. Suppose f is the output of RSE read operations.. It is used to fetch the queue index, say i, as shown in Figure 5.12 b., which, in turn, locates the pointer pointing to queue i s HOL packet, as shown in Figure 5.1 a.. Suppose FT is the virtual finish time of a new HOL packet. This packet s session index will be stored at the tail of the timing queue addressed by FT. In the meantime, the corresponding V-bit may be set to 1 write operations. if the timing queue was empty previously. The selector is used to choose the appropriate memory address during write and read operations. A similar calendar queue based on start times of the HOL packets can be used to find the minimum value of start times see Section 5.5.2.1., as required for updating V t . in 4.23.. The RSE is detailed below. 5.4.1 Hierarchical Searching In a calendar queue, a alidity bit V . is associated with each timing queue, indicating whether this queue is empty V s 0. or not V s 1.. Since packets





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Indications of numerical inaccuracy for a model may be found in the sensitivity of results to data perturbations, option variation, and variations in software and platforms used in estimation 5 Choose an optimization algorithm suited to your estimation problem Computational problems are often exacerbated by attempting to force a recalcitrant model into a limited statistical software application Seek out documentation of the algorithms supplied by your statistical package If your problem exceeds the capabilities of the software, seek a more appropriate algorithm rather than modifying your model (See 4 for a discussion of optimization algorithms) 6 Incorporate all sources of uncertainty in your estimates The most common computational techniques for generating point estimates and con dence regions assume a single mode of likelihood that is approximately locally quadratic Nonquadratic and/or multiple modes violate these assumptions and can cause reported con dence intervals to be much too narrow.

PACS and Imaging Informatics, Second Edition, by H. K. Huang Copyright 2010 John Wiley & Sons, Inc. 473

Thus:

Figure 5-7

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In addition to a further objective, Objective 7 (simplicity with constructive complexity) is developed further in this example. In particular, it provides a useful direct illustration of the notion of simplicity ef ciency . If we see the probability structures that estimates are based on as models, with a wide range of feasible choices, a rst-pass, constructively simple choice involves targeting a point on bNc in Figure 15.4. Choices on aNb are too simplistic to give enough insight. Later-pass choices should target a point on cNd . Choices like e are inef cient on any pass and should not be used. We start with an effective, constructively simple approach. We add constructive complexity where it pays, when it pays, using earlier passes to help manage the choice process with respect to ongoing iterative analysis. Simplicity ef ciency is the basis of risk management

0.87 0.66 0.53 0.60 0.35 0.26 0.57 0.53 0.54 0.51 0.44 0.41

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